# Russian researchers test multi-agent LLM system for neural-network tuning

> Researchers from Kurchatov, MEPhI and MIPT tuned selected components in fixed neural networks across five classification and physics tasks.

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- Section: Technology
- Country: Russia
- Published: 2026-10-12T23:33:38+00:00
- Updated: 2026-10-12T23:33:38+00:00
- Event / data period: 2026-10-09
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Researchers at the Kurchatov Institute, MEPhI and the Moscow Institute of Physics and Technology published a study on 9 October using multi-agent language models to tune selected neural-network components. The framework separates hypothesis generation, coding and experiment logging while leaving the network’s overall architecture fixed. The collaboration also includes a researcher from Harbin Institute of Technology in China.

Tests covered spiking-neural-network classification on MNIST and Digits, plus differential-equation problems using physics-informed neural networks. The paper reports improvements on some measures, but notes that the single-soliton test with an analytical solution allowed the known formula to be supplied and therefore does not measure a complex independent search. The results are experimental and limited to the tasks and configurations studied.

## Data

### Study scope

Two spiking-classification and three differential-equation tasks

Methodology: Count reported by the paper for its experimental scope; not a national statistic or field result.

| Indicator | Experimental scope count | Preprint publication date |
|---|---|---|
| Experimental tasks evaluated | 5 | 2026-10-09 |

Sources: <https://arxiv.org/html/2610.13663v1> · Download data: [JSON](https://newsverum.com/api/v1/articles/russian-researchers-test-llm-agents-for-neural-network-tuning/data/study-experimental-scope?lang=en&format=json) · [CSV](https://newsverum.com/api/v1/articles/russian-researchers-test-llm-agents-for-neural-network-tuning/data/study-experimental-scope?lang=en&format=csv)

## Sources

- [arXiv preprint by Kurchatov Institute, MEPhI and MIPT researchers](<https://arxiv.org/html/2610.13663v1>) (Source checked: 2026-10-13; Published: 2026-10-09)

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